Landslide susceptibility mapping using Naïve Bayes and Bayesian network models in Umyeonsan, Korea

References

• Ada M, San BT. 2018. Comparison of machine-learning techniques for landslide susceptibility mapping using two-level random sampling (2LRS) in Alakir Catchment Area, Antalya, Turkey. Nat Hazards. 90(1):237–263.
   Web of Science ®Google Scholar
• Althuwaynee OF, Pradhan B, Lee S. 2012. Application of an evidential belief function model in landslide susceptibility mapping. Comput Geosci. 44:120–135.
   Web of Science ®Google Scholar
• Anthes RA. 1984. Enhancement of convective precipitation by mesoscale variations in vegetative covering in semiarid regions. J Climate Appl Meteor. 23(4):541–554.
   Google Scholar
• Beven KJ, Kirkby MJ. 1979. A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d'appel variable de l'hydrologie du bassin versant. Hydrol Sci J. 24(1):43–69.
   Google Scholar
• Bhargavi P, Jyothi S. 2009. Applying naive bayes data mining technique for classification of agricultural land soils. Int J Comput Sci Netw Secur. 9:117–122.
   Google Scholar
• Böhner J, Selige T. 2006. Spatial prediction of soil attributes using terrain analysis and climate regionalisation. Gottinger Geographische Abhandlungen. 115:13–28.
   Google Scholar
• Bui DT, Tuan TA, Klempe H, Pradhan B, Revhaug I. 2016. Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides. 13:361–378.
   Web of Science ®Google Scholar
• Chen W, Peng J, Hong H, Shahabi H, Pradhan B, Liu J, Zhu A-X, Pei X, Duan Z. 2018a. Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China. Sci Total Environ. 626:1121–1135.
   PubMed Web of Science ®Google Scholar
• Chen W, Pourghasemi HR, Kornejady A, Zhang N. 2017a. Landslide spatial modeling: introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques. Geoderma. 305:314–327.
   Web of Science ®Google Scholar
• Chen W, Pourghasemi HR, Zhao Z. 2017b. A GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping. Geocarto Int. 32(4):367–385.
   Web of Science ®Google Scholar
• Chen W, Shahabi H, Shirzadi A, Li T, Guo C, Hong H, Li W, Pan D, Hui J, Ma M. 2018b. A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment. Geocarto Int. 33(12):1398–1420.
   Web of Science ®Google Scholar
• Choi J, Oh H-J, Lee H-J, Lee C, Lee S. 2012. Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Eng Geol. 124:12–23.
   Web of Science ®Google Scholar
• Chung C-J, Fabbri AG, Van Westen CJ. 1995. Multivariate regression analysis for landslide hazard zonation. In: Geographical information systems in assessing natural hazards. Dordrecht: Springer. p. 107–133.
   Google Scholar
• Conrad O, Bechtel B, Bock M, Dietrich H, Fischer E, Gerlitz L, Wehberg J, Wichmann V, Böhner J. 2015. System for automated geoscientific analyses (SAGA) v. 2.1. 4. Geosci Model Dev. 8(7):1991–2007.
   Web of Science ®Google Scholar
• Dahal RK, Hasegawa S, Nonomura A, Yamanaka M, Masuda T, Nishino K. 2008. GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping. Environ Geol. 54(2):311–324.
   Web of Science ®Google Scholar
• Devkota KC, Regmi AD, Pourghasemi HR, Yoshida K, Pradhan B, Ryu IC, Dhital MR, Althuwaynee OF. 2013. Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya. Nat Hazards. 65(1):135–165.
   Web of Science ®Google Scholar
• Dou J, Yamagishi H, Zhu Z, Yunus AP, Chen CW. 2018. TXT-tool 1.081-6.1: a comparative study of the binary logistic regression (BLR) and artificial neural network (ANN) Models for GIS-based spatial predicting landslides at a regional scale. In: Landslide dynamics: ISDR-ICL landslide interactive teaching tools. Dordrecht: Springer. p. 139–151.
   Google Scholar
• Feizizadeh B, Roodposhti MS, Blaschke T, Aryal J. 2017. Comparing GIS-based support vector machine kernel functions for landslide susceptibility mapping. Arab J Geosci. 10:122.
   Web of Science ®Google Scholar
• Gheisari S, Meybodi MR. 2016. Bnc-pso: structure learning of Bayesian networks by particle swarm optimization. Inf Sci. 348:272–289.
   Web of Science ®Google Scholar
• Han F, Zheng Y. 2018. Joint analysis of input and parametric uncertainties in watershed water quality modeling: a formal Bayesian approach. Adv Water Resour. 116:77–94.
   Web of Science ®Google Scholar
• Hjerdt K, McDonnell J, Seibert J, Rodhe A. 2004. A new topographic index to quantify downslope controls on local drainage. Water Resour Res. 40:1-6.
   Web of Science ®Google Scholar
• Hong H, Chen W, Xu C, Youssef AM, Pradhan B, Tien Bui D. 2017. Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio, certainty factor, and index of entropy. Geocarto Int. 32:139–154.
   Web of Science ®Google Scholar
• Hwang J, Kim T, Kim H, Cho B, Lee S. 2017. Predictive radon potential mapping in groundwater: a case study in Yongin, Korea. Environ Earth Sci. 76:515.
   Web of Science ®Google Scholar
• Jaafari A, Najafi A, Pourghasemi H, Rezaeian J, Sattarian A. 2014. GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, Northern Iran. Int J Environ Sci Technol. 11(4):909–926.
   Web of Science ®Google Scholar
• Jones D, Brunsden D, Goudie A. 1983. A preliminary geomorphological assessment of part of the Karakoram Highway. Quart J Eng Geol Hydrogeol. 16(4):331–355.
   Web of Science ®Google Scholar
• Kim J-C, Lee S, Jung H-S, Lee S. 2018. Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang, Korea. Geocarto Int. 33(9):1000–1015.
   Web of Science ®Google Scholar
• Korea Forest Service. 2013. 2013 detailed strategy for primary policy. Seoul: Korea Forest Service.
   Google Scholar
• Korean Geotechnical Society. 2011. Cause investigation of Umyeonsan landslide and recovery measure establishment service final report. Seoul: Korean Geotechnical Society.
   Google Scholar
• Lee J-M. 2004. An efficient algorithm for Naive Bayes with matrix transposition. J KIPS Trans. 11(1):117–124.
   Google Scholar
• Lee M-J, Park I, Won J-S, Lee S. 2016. Landslide hazard mapping considering rainfall probability in Inje, Korea. Geomat Nat Hazards Risk. 7(1):424–446.
   Web of Science ®Google Scholar
• Lee S, Chwae U, Min K. 2002. Landslide susceptibility mapping by correlation between topography and geological structure: the Janghung area, Korea. Geomorphology. 46(3-4):149–162.
   Web of Science ®Google Scholar
• Lee S, Hong S-M, Jung H-S. 2017. A support vector machine for landslide susceptibility mapping in Gangwon Province, Korea. Sustainability. 9(1):48.
   Web of Science ®Google Scholar
• Lee S, Lee M-J. 2017. Susceptibility mapping of Umyeonsan using logistic regression (LR) model and post-validation through field investigation. Korean J Remote Sens. 33:1047–1060.
   Google Scholar
• Lee S, Lee M-J, Lee S. 2018. Spatial prediction of urban landslide susceptibility based on topographic factors using boosted trees. Environ Earth Sci. 77:656.
   Web of Science ®Google Scholar
• Lee S, Min K. 2001. Statistical analysis of landslide susceptibility at Yongin, Korea. Environ Geol. 40(9):1095–1113.
   Web of Science ®Google Scholar
• Lee S, Ryu J-H, Won J-S, Park H-J. 2004. Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Eng Geol. 71(3-4):289–302.
   Web of Science ®Google Scholar
• Mezaal MR, Pradhan B. 2018. Data mining-aided automatic landslide detection using airborne laser scanning data in densely forested tropical areas. Korean J Remote Sens. 34:45–74.
   Web of Science ®Google Scholar
• Moore ID, Grayson R, Ladson A. 1991. Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process. 5(1):3–30.
   Web of Science ®Google Scholar
• Nami M, Jaafari A, Fallah M, Nabiuni S. 2018. Spatial prediction of wildfire probability in the Hyrcanian ecoregion using evidential belief function model and GIS. Int J Environ Sci Technol. 15(2):373–384.
   Web of Science ®Google Scholar
• Oh H-J, Kadavi PR, Lee C-W, Lee S. 2018. Evaluation of landslide susceptibility mapping by evidential belief function, logistic regression and support vector machine models. Geomat Nat Hazards Risk. 9(1):1053–1070.
   Web of Science ®Google Scholar
• Oh H-J, Lee S. 2017. Shallow landslide susceptibility modeling using the data mining models artificial neural network and boosted tree. Appl Sci. 7(10):1000.
   Google Scholar
• Oommen T, Cobin PF, Gierke JS, Sajinkumar K. 2018. Significance of variable selection and scaling issues for probabilistic modeling of rainfall-induced landslide susceptibility. Spat Inf Res. 26(1):21–31.
   Web of Science ®Google Scholar
• Ortiz JAV, Martínez-Graña AM. 2018. A neural network model applied to landslide susceptibility analysis (Capitanejo, Colombia). Geomat Nat Hazards Risk. 9(1):1106-1128.
   Google Scholar
• Ozdemir A. 2011. Landslide susceptibility mapping using Bayesian approach in the Sultan Mountains (Akşehir, Turkey). Nat Hazards. 59(3):1573–1607.
   Web of Science ®Google Scholar
• Pachauri RK, Allen MR, Barros VR, Broome J, Cramer W, Christ R, Church JA, Clarke L, Dahe Q, Dasgupta P. 2014. Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change: IPCC.
   Google Scholar
• Park I, Lee S. 2014. Spatial prediction of landslide susceptibility using a decision tree approach: a case study of the Pyeongchang area, Korea. Int J Remote Sens. 35(16):6089–6112.
   Web of Science ®Google Scholar
• Park S-J, Lee C-W, Lee S, Lee M-J. 2018. Landslide susceptibility mapping and comparison using decision tree models: a case study of Jumunjin area, Korea. Remote Sens. 10(10):1545.
   Web of Science ®Google Scholar
• Pearl J. 2014. Probabilistic reasoning in intelligent systems: networks of plausible inference. Amsterdam: Elsevier; p. 116-130.
   Google Scholar
• Perlich C, Provost F, Simonoff JS. 2003. Tree induction vs. logistic regression: A learning-curve analysis. J Mach Learn Res. 4:211–255.
   Web of Science ®Google Scholar
• Pham BT, Bui DT, Prakash I, Dholakia M. 2017. Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. Catena. 149:52–63.
   Web of Science ®Google Scholar
• Pradhan AMS, Kang H-S, Lee S, Kim Y-T. 2017a. Spatial model integration for shallow landslide susceptibility and its runout using a GIS-based approach in Yongin, Korea. Geocarto Int. 32(4):420–441.
   Web of Science ®Google Scholar
• Pradhan B. 2010. Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia. Adv Space Res. 45(10):1244–1256.
   Web of Science ®Google Scholar
• Pradhan B, Jebur MN, Abdullahi S. 2017b. Spatial prediction of landslides along Jalan Kota in Bandar Seri Begawan (Brunei) using airborne LiDAR data and support vector machine. In: Laser scanning applications in landslide assessment. Dordrecht: Springer; p. 167–178.
   Google Scholar
• Ransom KM, Bell AM, Barber QE, Kourakos G, Harter T. 2018. A Bayesian approach to infer nitrogen loading rates from crop and land-use types surrounding private wells in the Central Valley, California. Hydrol Earth Syst Sci. 22(5):2739–2758.
   Web of Science ®Google Scholar
• Schlögel R, Marchesini I, Alvioli M, Reichenbach P, Rossi M, Malet J-P. 2018. Optimizing landslide susceptibility zonation: Effects of DEM spatial resolution and slope unit delineation on logistic regression models. Geomorphology. 301:10–20.
   Web of Science ®Google Scholar
• Seoul Special City. 2014. Supplemental investigation report of causes of Umyeonsan landslides. Seoul: Seoul Special City.
   Google Scholar
• Sikorska A, Seibert J. 2018. Value of different precipitation data for flood prediction in an alpine catchment: a Bayesian approach. J Hydrol. 556:961–971.
   Web of Science ®Google Scholar
• Soni J, Ansari U, Sharma D, Soni S. 2011. Predictive data mining for medical diagnosis: an overview of heart disease prediction. IJCA. 17(8):43–48.
   Google Scholar
• Truong XL, Mitamura M, Kono Y, Raghavan V, Yonezawa G, Truong XQ, Do TH, Tien Bui D, Lee S. 2018. Enhancing prediction performance of landslide susceptibility model using hybrid machine learning approach of bagging ensemble and logistic model tree. Appl Sci. 8(7):1046.
   Google Scholar
• Tsangaratos P, Ilia I. 2016. Landslide susceptibility mapping using a modified decision tree classifier in the Xanthi Perfection, Greece. Landslides. 13(2):305–320.
   Web of Science ®Google Scholar
• UNISDR CRED. 2015. The human cost of natural disasters: a global perspective. Geneva: CRED.
   Google Scholar
• Varnes DJ. 1958. Landslide types and processes. Landslides Eng Pract. 29:20–45.
   Google Scholar
• Wang L-J, Guo M, Sawada K, Lin J, Zhang J. 2016. A comparative study of landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network. Geosci J. 20(1):117–136.
   Web of Science ®Google Scholar
• Wischmeier WH, Smith DD. 1978. Predicting rainfall erosion losses-a guide to conservation planning. Predicting rainfall erosion losses-a guide to conservation planning.
   Google Scholar
• Witten IH, Frank E, Hall MA, Pal CJ. 2016. Data mining: practical machine learning tools and techniques. Amsterdam: Morgan Kaufmann.
   Google Scholar
• Youssef AM, Pourghasemi HR, Pourtaghi ZS, Al-Katheeri MM. 2016. Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslides. 13(5):839–856.
   Web of Science ®Google Scholar
• Zhou S, Chen G, Fang L. 2016. Distribution pattern of landslides triggered by the 2014 Ludian earthquake of China: implications for regional threshold topography and the seismogenic fault identification. IJGI. 5(4):46.
   Web of Science ®Google Scholar